Learning-Based Joint Optimization of Transmit Power and Harvesting Time in Wireless-Powered Networks With Co-Channel Interference
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[1] Nei Kato,et al. Model Predictive Joint Transmit Power Control for Improving System Availability in Energy-Harvesting Wireless Mesh Networks , 2018, IEEE Communications Letters.
[2] Jörg Fliege,et al. Complexity of gradient descent for multiobjective optimization , 2018, Optim. Methods Softw..
[3] Kisong Lee,et al. Proportional Fair Energy-Efficient Resource Allocation in Energy-Harvesting-Based Wireless Networks , 2018, IEEE Systems Journal.
[4] Bin Han,et al. Applying Device-to-Device Communication to Enhance IoT Services , 2017, IEEE Communications Standards Magazine.
[5] Xiaodong Wang,et al. Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks , 2009, IEEE Transactions on Vehicular Technology.
[6] Tony Q. S. Quek,et al. Deep Learning for Distributed Optimization: Applications to Wireless Resource Management , 2019, IEEE Journal on Selected Areas in Communications.
[7] H. Vincent Poor,et al. Spectral and Energy Efficiencies in Full-Duplex Wireless Information and Power Transfer , 2017, IEEE Transactions on Communications.
[8] Walid Saad,et al. Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective , 2017, IEEE Transactions on Wireless Communications.
[9] Eduardo Tovar,et al. On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection , 2019, IEEE Transactions on Vehicular Technology.
[10] Woongsup Lee,et al. Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network , 2018, IEEE Communications Letters.
[11] Yuanming Shi,et al. LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples , 2018, IEEE Transactions on Wireless Communications.
[12] Zhu Han,et al. Wireless Networks With RF Energy Harvesting: A Contemporary Survey , 2014, IEEE Communications Surveys & Tutorials.
[13] Paul de Kerret,et al. Team Deep Neural Networks for Interference Channels , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).
[14] Arumugam Nallanathan,et al. Energy-Efficient D2D Communications Underlaying NOMA-Based Networks With Energy Harvesting , 2018, IEEE Communications Letters.
[15] Woongsup Lee,et al. Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication , 2018, IEEE Wireless Communications Letters.
[16] I. Stancu-Minasian. Nonlinear Fractional Programming , 1997 .
[17] Kee Chaing Chua,et al. Wireless information transfer with opportunistic energy harvesting , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.
[18] N. Sidiropoulos,et al. Learning to Optimize: Training Deep Neural Networks for Interference Management , 2017, IEEE Transactions on Signal Processing.
[19] Nei Kato,et al. State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.
[20] Nicholas I. M. Gould,et al. On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems , 2010, SIAM J. Optim..
[21] Mingyu Li,et al. Deep Reinforcement Learning Optimal Transmission Policy for Communication Systems With Energy Harvesting and Adaptive MQAM , 2019, IEEE Transactions on Vehicular Technology.